cpfiffer

"""
pip install outlines torch==2.4.0 transformers accelerate typing-extensions pillow pdf2image rich requests

may need to install tkinter: https://stackoverflow.com/questions/25905540/importerror-no-module-named-tkinter

sudo apt-get install poppler-utils
"""

from enum import Enum
from io import BytesIO

kathsherratt

---
title: "Appendix: Responses to improving conditions for outbreak modelling"
date: "2024"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
library(knitr)
library(googlesheets4)

adamkucharski

# Example code for simulation recovery of infection dynamics from deaths for COVID -----------------------------------------

# Load libraries:

# install.packages("EpiNow2", repos = "https://epiforecasts.r-universe.dev")
library(EpiNow2)

# Simulate data and delays -----------------------------------------

# Simulate infections with a sharp drop

sbfnk

#' Aggregate daily forecasts to weekly forecasts
#' 
#' Aggregate daily case forecasts from regional_epinow2 to weekly forecasts.
#' 
#' @param inf List output from `EpiNow2::regional_epinow()`. No default.
#' @param week_start Week start day. Default is 1 (Monday).
#' @param summary_quantiles Quantiles for forecast summary. Default is median, plus upper and lower bound for 20\%, 50\% and 90\% forecasts. 
#' @importFrom dplyr group_by summarise filter mutate select case_when contains
#' @importFrom tidyr pivot_wider
#' @importFrom purrr map_df

zsusswein

functions {
  vector convolve(vector a, vector b) {
    int na = num_elements(a);       // Vector lengths
    int nb = num_elements(b);
    int n_zero_a = nb - 1;          // Zero padding lengths
    int n_zero_b = na - 1;

    vector[nb] b_rev = reverse(b); // The reversed b vector

    vector[na + n_zero_a] a_pad;    // Instantiate zero padded vectors

swo

import polars as pl

# Making data frames ------------------------------------------
#  with dictionary
df = pl.DataFrame({
    'name': ['foo', 'bar', 'baz'],
    'bar': [0, 1, 2],
    'qux': [0.0, 1.0, 2.0]
})

jonathonmellor

wd <- system("echo $(git rev-parse --show-toplevel)", intern = TRUE)
setwd(wd)
source(paste0(wd, "/influenza/scripts/depends.R"))

output_dir <- fs::path(paste0(wd, "/influenza/papers/univariate/outputs"))

# nhs_data path for getting population sizes:
nhs_data_path <- "" # Removed path to influenza data + population catchments per trust

medewitt

# Purpose: This is just a play model to think about time spent on large and big grants/opportunities and 
#    overall likelihood of a successful result (undefined, just general feel).
#    2080 is the hours in a year if working 40 hours (because everyone does that...)

using JuMP
using Ipopt
model = Model(Ipopt.Optimizer)

p1 = .05 # Probability of winning grant 1
sx1 = .4 # Probability of successful work (class 1)

sbfnk

##' Get estimates shown at \url{https://epiforecasts.io/covid} for all times
##'
##' Nowcasts at \url{https://epiforecasts.covid} and the related github repository at
##' \url{https://github.com/epiforecasts/covid-rt-estimates} only cover the last 3
##' months. This function downloads all available estimates and applies a median
##' average to the provided quantiles to provide an estimate of a time series
##' covering all times available
##'
##' @param dataset character; data set corresponding to directories at
##' \url{https://github.com/epiforecasts/covid-rt-estimates}. Default is

sbfnk

library("gh")
library("vroom")

owner <- "epiforecasts"
repo <- "covid-rt-estimates"
path <- "subnational/united-kingdom-local/cases/summary/rt.csv"

rt_commits <-
  gh("/repos/{owner}/{repo}/commits?path={path}",
     owner = owner,